




"""simple docstring"""


import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class    lowercase(	unittest.TestCase					):







 '''simple docstring'''





 lowercase__					   =   MODEL_FOR_MASKED_LM_MAPPING
 lowercase__					   =   TF_MODEL_FOR_MASKED_LM_MAPPING


 def 					UpperCamelCase_  (							self:						Optional[int]						):




   '''simple docstring'''



   super().tearDown()
   # clean-up as much as possible GPU memory occupied by PyTorch
   gc.collect()
   if is_torch_available():
     import torch

     torch.cuda.empty_cache()


 @require_tf
 def 					UpperCamelCase_  (							self:						List[str]						):




   '''simple docstring'''



   _snake_case				:						Union[str, Any]								=    pipeline(task="""fill-mask""", model="""sshleifer/tiny-distilroberta-base""", top_k=2, framework="""tf"""						)
   _snake_case				:						Dict								=    unmasker("""My name is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 38_015, """token_str""": """ grouped"""},
           {"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 25_506, """token_str""": """ accuser"""},
       ], )

   _snake_case				:						Optional[int]								=    unmasker("""The largest city in France is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {
               """sequence""": """The largest city in France is grouped""",
               """score""": 2.1E-05,
               """token""": 38_015,
               """token_str""": """ grouped""",
           },
           {
               """sequence""": """The largest city in France is accuser""",
               """score""": 2.1E-05,
               """token""": 25_506,
               """token_str""": """ accuser""",
           },
       ], )

   _snake_case				:						int								=    unmasker("""My name is <mask>""", targets=[""" Patrick""", """ Clara""", """ Teven"""], top_k=3						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13_606, """token_str""": """ Clara"""},
           {"""sequence""": """My name is Patrick""", """score""": 2E-05, """token""": 3_499, """token_str""": """ Patrick"""},
           {"""sequence""": """My name is Te""", """score""": 1.9E-05, """token""": 2_941, """token_str""": """ Te"""},
       ], )


 @require_torch
 def 					UpperCamelCase_  (							self:						Optional[Any]						):




   '''simple docstring'''



   _snake_case				:						Tuple								=    pipeline(task="""fill-mask""", model="""sshleifer/tiny-distilroberta-base""", top_k=2, framework="""pt"""						)

   _snake_case				:						Dict								=    unmasker("""My name is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 35_676, """token_str""": """ Maul"""},
           {"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS"""},
       ], )

   _snake_case				:						Dict								=    unmasker("""The largest city in France is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {
               """sequence""": """The largest city in France is Maul""",
               """score""": 2.2E-05,
               """token""": 35_676,
               """token_str""": """ Maul""",
           },
           {"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS"""},
       ], )

   _snake_case				:						List[Any]								=    unmasker("""My name is <mask>""", targets=[""" Patrick""", """ Clara""", """ Teven"""], top_k=3						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           {"""sequence""": """My name is Patrick""", """score""": 2.1E-05, """token""": 3_499, """token_str""": """ Patrick"""},
           {"""sequence""": """My name is Te""", """score""": 2E-05, """token""": 2_941, """token_str""": """ Te"""},
           {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13_606, """token_str""": """ Clara"""},
       ], )

   _snake_case				:						Optional[int]								=    unmasker("""My name is <mask> <mask>""", top_k=2						)

   self.assertEqual(
       nested_simplify(_UpperCAmelCase, decimals=6						), [
           [
               {
                   """score""": 2.2E-05,
                   """token""": 35_676,
                   """token_str""": """ Maul""",
                   """sequence""": """<s>My name is Maul<mask></s>""",
               },
               {"""score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
           ],
           [
               {
                   """score""": 2.2E-05,
                   """token""": 35_676,
                   """token_str""": """ Maul""",
                   """sequence""": """<s>My name is<mask> Maul</s>""",
               },
               {"""score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
           ],
       ], )


 @require_torch_gpu
 def 					UpperCamelCase_  (							self:						List[Any]						):




   '''simple docstring'''



   _snake_case				:						Optional[int]								=    pipeline("""fill-mask""", model="""hf-internal-testing/tiny-random-distilbert""", device=0, framework="""pt"""						)

   # convert model to fp16
   pipe.model.half()

   _snake_case				:						Union[str, Any]								=    pipe("""Paris is the [MASK] of France."""						)
   # We actually don't care about the result, we just want to make sure
   # it works, meaning the float16 tensor got casted back to float32
   # for postprocessing.
   self.assertIsInstance(_UpperCAmelCase, _UpperCAmelCase						)


 @slow
 @require_torch
 def 					UpperCamelCase_  (							self:						Optional[Any]						):




   '''simple docstring'''



   _snake_case				:						Any								=    pipeline(task="""fill-mask""", model="""distilroberta-base""", top_k=2, framework="""pt"""						)
   self.run_large_test(_UpperCAmelCase						)


 @slow
 @require_tf
 def 					UpperCamelCase_  (							self:						List[Any]						):




   '''simple docstring'''



   _snake_case				:						Dict								=    pipeline(task="""fill-mask""", model="""distilroberta-base""", top_k=2, framework="""tf"""						)
   self.run_large_test(_UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						int, a_:						Union[str, Any]						):




   '''simple docstring'''



   _snake_case				:						str								=    unmasker("""My name is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase						), [
           {"""sequence""": """My name is John""", """score""": 0.008, """token""": 610, """token_str""": """ John"""},
           {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1_573, """token_str""": """ Chris"""},
       ], )
   _snake_case				:						Union[str, Any]								=    unmasker("""The largest city in France is <mask>"""						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase						), [
           {
               """sequence""": """The largest city in France is Paris""",
               """score""": 0.251,
               """token""": 2_201,
               """token_str""": """ Paris""",
           },
           {
               """sequence""": """The largest city in France is Lyon""",
               """score""": 0.214,
               """token""": 12_790,
               """token_str""": """ Lyon""",
           },
       ], )

   _snake_case				:						Dict								=    unmasker("""My name is <mask>""", targets=[""" Patrick""", """ Clara""", """ Teven"""], top_k=3						)
   self.assertEqual(
       nested_simplify(_UpperCAmelCase						), [
           {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3_499, """token_str""": """ Patrick"""},
           {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13_606, """token_str""": """ Clara"""},
           {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_941, """token_str""": """ Te"""},
       ], )


 @require_torch
 def 					UpperCamelCase_  (							self:						Any						):




   '''simple docstring'''



   _snake_case				:						Optional[Any]								=    pipeline(task="""fill-mask""", model="""sshleifer/tiny-distilroberta-base""", framework="""pt"""						)
   _snake_case				:						List[str]								=    None
   _snake_case				:						int								=    None
   self.run_pipeline_test(_UpperCAmelCase, []						)


 @require_tf
 def 					UpperCamelCase_  (							self:						Tuple						):




   '''simple docstring'''



   _snake_case				:						List[str]								=    pipeline(task="""fill-mask""", model="""sshleifer/tiny-distilroberta-base""", framework="""tf"""						)
   _snake_case				:						Optional[Any]								=    None
   _snake_case				:						Optional[int]								=    None
   self.run_pipeline_test(_UpperCAmelCase, []						)


 def 					UpperCamelCase_  (							self:						Dict, a_:						Dict, a_:						Tuple, a_:						Tuple						):




   '''simple docstring'''



   if tokenizer is None or tokenizer.mask_token_id is None:
     self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""						)

   _snake_case				:						List[str]								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)
   _snake_case				:						List[str]								=    [
       f"This is another {tokenizer.mask_token} test",
   ]
   return fill_masker, examples


 def 					UpperCamelCase_  (							self:						int, a_:						List[Any], a_:						Optional[Any]						):




   '''simple docstring'''



   _snake_case				:						Tuple								=    fill_masker.tokenizer
   _snake_case				:						str								=    fill_masker.model

   _snake_case				:						int								=    fill_masker(
       f"This is a {tokenizer.mask_token}", )
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )

   _snake_case				:						Optional[Any]								=    fill_masker([f"This is a {tokenizer.mask_token}"]						)
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )

   _snake_case				:						Optional[Any]								=    fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."]						)
   self.assertEqual(
       _UpperCAmelCase, [
           [
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           ],
           [
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           ],
       ], )

   with self.assertRaises(_UpperCAmelCase						):
     fill_masker([None]						)
   # No mask_token is not supported
   with self.assertRaises(_UpperCAmelCase						):
     fill_masker("""This is"""						)

   self.run_test_top_k(_UpperCAmelCase, _UpperCAmelCase						)
   self.run_test_targets(_UpperCAmelCase, _UpperCAmelCase						)
   self.run_test_top_k_targets(_UpperCAmelCase, _UpperCAmelCase						)
   self.fill_mask_with_duplicate_targets_and_top_k(_UpperCAmelCase, _UpperCAmelCase						)
   self.fill_mask_with_multiple_masks(_UpperCAmelCase, _UpperCAmelCase						)


 def 					UpperCamelCase_  (							self:						List[str], a_:						Union[str, Any], a_:						Dict						):




   '''simple docstring'''



   _snake_case				:						Union[str, Any]								=    tokenizer.get_vocab()
   _snake_case				:						Optional[Any]								=    sorted(vocab.keys()						)[:2]
   # Pipeline argument
   _snake_case				:						str								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase, targets=_UpperCAmelCase						)
   _snake_case				:						int								=    fill_masker(f"This is a {tokenizer.mask_token}"						)
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )
   _snake_case				:						Optional[int]								=    {vocab[el] for el in targets}
   self.assertEqual({el["""token"""] for el in outputs}, _UpperCAmelCase						)
   _snake_case				:						Optional[int]								=    [tokenizer.decode([x]						) for x in target_ids]
   self.assertEqual({el["""token_str"""] for el in outputs}, set(_UpperCAmelCase						)						)

   # Call argument
   _snake_case				:						Union[str, Any]								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)
   _snake_case				:						List[Any]								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=_UpperCAmelCase						)
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )
   _snake_case				:						Dict								=    {vocab[el] for el in targets}
   self.assertEqual({el["""token"""] for el in outputs}, _UpperCAmelCase						)
   _snake_case				:						str								=    [tokenizer.decode([x]						) for x in target_ids]
   self.assertEqual({el["""token_str"""] for el in outputs}, set(_UpperCAmelCase						)						)

   # Score equivalence
   _snake_case				:						List[str]								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=_UpperCAmelCase						)
   _snake_case				:						Tuple								=    [top_mask["""token_str"""] for top_mask in outputs]
   _snake_case				:						Dict								=    [top_mask["""score"""] for top_mask in outputs]

   # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
   if set(_UpperCAmelCase						) == set(_UpperCAmelCase						):
     _snake_case				:						Dict								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=_UpperCAmelCase						)
     _snake_case				:						str								=    [top_mask["""score"""] for top_mask in unmasked_targets]
     self.assertEqual(nested_simplify(_UpperCAmelCase						), nested_simplify(_UpperCAmelCase						)						)

   # Raises with invalid
   with self.assertRaises(_UpperCAmelCase						):
     _snake_case				:						Optional[Any]								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=[]						)
   # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
   if "" not in tokenizer.get_vocab():
     with self.assertRaises(_UpperCAmelCase						):
       _snake_case				:						int								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=[""""""]						)
     with self.assertRaises(_UpperCAmelCase						):
       _snake_case				:						Union[str, Any]								=    fill_masker(f"This is a {tokenizer.mask_token}", targets=""""""						)


 def 					UpperCamelCase_  (							self:						Union[str, Any], a_:						Optional[Any], a_:						str						):




   '''simple docstring'''



   _snake_case				:						List[Any]								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase, top_k=2						)
   _snake_case				:						Dict								=    fill_masker(f"This is a {tokenizer.mask_token}"						)
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )

   _snake_case				:						int								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)
   _snake_case				:						Optional[int]								=    fill_masker(f"This is a {tokenizer.mask_token}", top_k=2						)
   self.assertEqual(
       _UpperCAmelCase, [
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
       ], )
   self.assertEqual(nested_simplify(_UpperCAmelCase						), nested_simplify(_UpperCAmelCase						)						)


 def 					UpperCamelCase_  (							self:						Any, a_:						Optional[Any], a_:						Optional[Any]						):




   '''simple docstring'''



   _snake_case				:						List[Any]								=    tokenizer.get_vocab()
   _snake_case				:						str								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)

   # top_k=2, ntargets=3
   _snake_case				:						Optional[Any]								=    sorted(vocab.keys()						)[:3]
   _snake_case				:						List[Any]								=    fill_masker(f"This is a {tokenizer.mask_token}", top_k=2, targets=_UpperCAmelCase						)

   # If we use the most probably targets, and filter differently, we should still
   # have the same results
   _snake_case				:						Dict								=    [el["""token_str"""] for el in sorted(_UpperCAmelCase, key=lambda a_						: x["score"], reverse=_UpperCAmelCase						)]
   # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
   if set(_UpperCAmelCase						).issubset(_UpperCAmelCase						):
     _snake_case				:						str								=    fill_masker(f"This is a {tokenizer.mask_token}", top_k=3, targets=_UpperCAmelCase						)
     # They should yield exactly the same result
     self.assertEqual(nested_simplify(_UpperCAmelCase						), nested_simplify(_UpperCAmelCase						)						)


 def 					UpperCamelCase_  (							self:						str, a_:						int, a_:						Union[str, Any]						):




   '''simple docstring'''



   _snake_case				:						Optional[Any]								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)
   _snake_case				:						List[str]								=    tokenizer.get_vocab()
   # String duplicates + id duplicates
   _snake_case				:						List[Any]								=    sorted(vocab.keys()						)[:3]
   _snake_case				:						List[str]								=    [targets[0], targets[1], targets[0], targets[2], targets[1]]
   _snake_case				:						Optional[int]								=    fill_masker(f"My name is {tokenizer.mask_token}", targets=_UpperCAmelCase, top_k=10						)

   # The target list contains duplicates, so we can't output more
   # than them
   self.assertEqual(len(_UpperCAmelCase						), 3						)


 def 					UpperCamelCase_  (							self:						Optional[Any], a_:						Optional[Any], a_:						List[Any]						):




   '''simple docstring'''



   _snake_case				:						Any								=    FillMaskPipeline(model=_UpperCAmelCase, tokenizer=_UpperCAmelCase						)

   _snake_case				:						int								=    fill_masker(
       f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}", top_k=2						)
   self.assertEqual(
       _UpperCAmelCase, [
           [
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           ],
           [
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           ],
           [
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
               {"""sequence""": ANY(_UpperCAmelCase						), """score""": ANY(_UpperCAmelCase						), """token""": ANY(_UpperCAmelCase						), """token_str""": ANY(_UpperCAmelCase						)},
           ],
       ], )







'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




